code: def forward(self, sample_list):
      sample_list.text = self.word_embedding(sample_list.text)
      text_embedding_total = self.process_text_embedding(sample_list)
  
      image_embedding_total, _ = self.process_feature_embedding(
          "image", sample_list, text_embedding_total
      )
  
      context_embedding_total, _ = self.process_feature_embedding(
          "context",
          sample_list,
          text_embedding_total,
          ["order_vectors"],
      )
  
      if self.inter_model is not None:
          image_embedding_total = self.inter_model(
              image_embedding_total
          )
  
      joint_embedding = self.combine_embeddings(
          ["image", "text"],
          [
              image_embedding_total,
              text_embedding_total,
              context_embedding_total,
          ],
      )
  
      scores = self.calculate_logits(joint_embedding)
  
      return {"scores": scores}
  
file_path: mmf/mmf/models/lorra.py
imports: from mmf.common.registry import registry
  from mmf.models.pythia import Pythia
label_desc: Use image node_id 6 for calling the LoRRA obj's underlying member method code with example usage: obj.forward(sample_list) and returns: dict